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MoleculeNet: a benchmark for molecular machine learning

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Abstract

Molecular machine learning has been maturing rapidly over the last few years. Improved methods and the presence of larger datasets have enabled machine learning algorithms to make increasingly accurate predictions about molecular properties. However, algorithmic progress has been limited due to the lack of a standard benchmark to compare the efficacy of proposed methods; most new algorithms are benchmarked on different datasets making it challenging to gauge the quality of proposed methods. This work introduces MoleculeNet, a large scale benchmark for molecular machine learning. MoleculeNet curates multiple public datasets, establishes metrics for evaluation, and offers high quality open-source implementations of multiple previously proposed molecular featurization and learning algorithms (released as part of the DeepChem open source library). MoleculeNet benchmarks demonstrate that learnable representations are powerful tools for molecular machine learning and broadly offer the best performance. However, this result comes with caveats. Learnable representations still struggle to deal with complex tasks under data scarcity and highly imbalanced classification. For quantum mechanical and biophysical datasets, the use of physics-aware featurizations can be more important than choice of particular learning algorithm.

Graphical abstract: MoleculeNet: a benchmark for molecular machine learning

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Publication details

The article was received on 15 Jun 2017, accepted on 30 Oct 2017 and first published on 31 Oct 2017


Article type: Edge Article
DOI: 10.1039/C7SC02664A
Citation: Chem. Sci., 2018, Advance Article
  • Open access: Creative Commons BY-NC license
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    MoleculeNet: a benchmark for molecular machine learning

    Z. Wu, B. Ramsundar, Evan N. Feinberg, J. Gomes, C. Geniesse, A. S. Pappu, K. Leswing and V. Pande, Chem. Sci., 2018, Advance Article , DOI: 10.1039/C7SC02664A

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